A relatively large number of analysts may be proficient in utilizing spreadsheet applications to carry out data analysis, for example Microsoft® Excel. An analyst for example may be a statistician, an engineer, or a scientist, but may be more commonly a sales or marketing professional, an accountant, or other business analyst. More than any other time there are opportunities to mine or derive insight or businesses intelligence from a dataset. The dataset for example may be a large dataset with information about customers, financial transactions, social interactions (e.g., messages or connections on social media), ecological data, medical data, and similar datasets. For example, one can define a model which takes in data of a customer information dataset, applies a set of formulas and/or algorithms, and which outputs a prediction metric for lifetime customer value or a relative value score.
However, these datasets can be quite large and difficult to work with. The analyst may have difficulty getting the data in a usable form. For example, Excel® may have difficulty with 60,000 rows of data. The analyst may be unsure what existing model to apply, how to propose a new model, and how to prototype the model. Attempts to apply the model to a dataset may result in errors or freezes in the spreadsheet application, or a complete crash of a computing device of the analyst.
Rather, to work with the dataset effectively certain skill may be required. For example, a ‘big data’ analyst may needed. The big data analyst may possess such skills as: fluency in structured query language (SQL) to access and curate the dataset from a database, proficiency in a programming language (e.g., Python) to build the model, knowledge of Hadoop® and/or Map-reduce methods to apply the model to the dataset, and even an understanding of cloud computing infrastructure to achieve fast and/or efficient results. Much of the work of the big data analyst may occur on a command line interface. The big data analyst may have advanced degrees, may be in high demand, may command a significant salary, and ultimately may be difficult to hire.
As a result, analysts without specialized skill may have difficulty analyzing datasets, even when they are otherwise qualified or expert at analyzing data. Organizations such as companies may have difficulty deriving meaning, intelligence, or value from their data, causing missed opportunities, slower growth, and organization-wide value decline.